example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LCOE Estimator Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# @title Clone repository (hidden in Colab)\n",
    "try:\n",
    "    import google.colab\n",
    "\n",
    "    IN_COLAB = True\n",
    "except ImportError:\n",
    "    IN_COLAB = False\n",
    "\n",
    "if IN_COLAB:\n",
    "    !git clone https://github.com/tannhorn/ele-cost.git\n",
    "    %cd ele-cost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/tannhorn/ele-cost/blob/main/example.ipynb) [![GitHub](https://img.shields.io/badge/GitHub-black?logo=github)](https://github.com/tannhorn/ele-cost/blob/main/example.ipynb)\n",
    "\n",
    "This is a simple example walkthrough of how to use the code in this repo to calculate LCOE. Python knowledge is beneficial, of course, but not completely necessary. Most of the cells are self-explanatory even without coding skills and you can just run them and read through. And maybe the rest will spur your desire to learn some Python :-)?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by importing the functionality and some other packages we will need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import plotly.graph_objects as go\n",
    "import plotly.subplots as sp\n",
    "\n",
    "from lcoe import evaluate as evl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's introduce necessary data. They are based on the information presented in [IEA/OECD-NEA report](https://iea.blob.core.windows.net/assets/ae17da3d-e8a5-4163-a3ec-2e6fb0b5677d/Projected-Costs-of-Generating-Electricity-2020.pdf) titled \"Projected Costs of Generating Electricity 2020,\". They should be reasonably corresponding to a 1000 MWe LWR project at pre-2020 prices (see the report for a compendium of real-world data).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We assume discount rate of 7% and calculate everything relative to now, taken as year 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "discount_rate = 0.07\n",
    "present_year = 1\n",
    "\n",
    "power = 1000  # MWe\n",
    "power_kw = power * 1000  # kWe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The project will consist of three phases: construction, operations, and decommissioning. We introduce their durations in years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "construction_duration = 10  # years\n",
    "operational_lifetime = 60  # years\n",
    "decommissioning_duration = 10  # years"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Project Costs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first chunk of costs will be the capital costs. We use the supplied information on overnight construction costs and calculate capital cost per year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "overnight_cost_per_kwe = 8600  # $/kWe, median\n",
    "overnight_cost = overnight_cost_per_kwe * power_kw\n",
    "\n",
    "capital_cost_per_year = overnight_cost / construction_duration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Second chunk of costs will be O&M and fuel costs. For that we need to calculate energy production per year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "capacity_factor = 0.85\n",
    "hours_in_year = 8760\n",
    "mwh_per_year = hours_in_year * power * capacity_factor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This can now be used to estimate the O&M and fuel costs, those including both front-end and back-end costs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# o&m\n",
    "o_and_m_cost_per_kw_year = 80  # $/kwe/year\n",
    "o_and_m_cost_per_year = o_and_m_cost_per_kw_year * power_kw\n",
    "\n",
    "# fuel\n",
    "front_end_fuel_cost_per_mwh = 7  # $/MWh\n",
    "back_end_fuel_cost_per_mwh = 2.33  # $/MWh\n",
    "total_fuel_cost_per_mwh = front_end_fuel_cost_per_mwh + back_end_fuel_cost_per_mwh\n",
    "\n",
    "fuel_cost_per_year = total_fuel_cost_per_mwh * mwh_per_year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another cost item considered in the IEA report is carbon cost. This is of course not very relevant for a nuclear power plant, but we put the cost item here as well, for completeness."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# carbon\n",
    "carbon_cost_per_ton = 30  # $/t CO2\n",
    "carbon_content_per_mwh = 0  # t/MWh\n",
    "carbon_cost_per_mwh = carbon_cost_per_ton * carbon_content_per_mwh\n",
    "\n",
    "carbon_cost_per_year = carbon_cost_per_mwh * mwh_per_year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The final cost item we consider is decommissioning, for which an estimate is produced based on the overnight cost and a multiplier factor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# decommissioning\n",
    "decommissioning_cost_factor = 0.15\n",
    "decommissioning_cost = decommissioning_cost_factor * overnight_cost\n",
    "decommissioning_cost_per_year = decommissioning_cost / decommissioning_duration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cash and Revenue Flows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are all the cost items we need; now we define the timeline of the project, including construction, operation and decommissioning (each of the phases assumed to start immediately after the previous one)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# timeline\n",
    "construction_start = present_year\n",
    "construction_end = present_year + construction_duration - 1\n",
    "operation_start = construction_end + 1\n",
    "operation_end = operation_start + operational_lifetime - 1\n",
    "decommissioning_start = operation_end + 1\n",
    "decommissioning_end = decommissioning_start + decommissioning_duration - 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The timeline, together with the cost items, will now be used to create cash flows in time. For that, the functionality provided in this repo will be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cash flows\n",
    "cost_items_rolled = {\n",
    "    \"Capital\": (construction_start, construction_end, capital_cost_per_year),\n",
    "    \"O&M\": (operation_start, operation_end, o_and_m_cost_per_year),\n",
    "    \"Fuel\": (operation_start, operation_end, fuel_cost_per_year),\n",
    "    \"Carbon\": (operation_start, operation_end, carbon_cost_per_year),\n",
    "    \"Decommissioning\": (\n",
    "        decommissioning_start,\n",
    "        decommissioning_end,\n",
    "        decommissioning_cost_per_year,\n",
    "    ),\n",
    "}\n",
    "\n",
    "# Create the cost_items dictionary from cost_items_rolled\n",
    "cost_items = evl.create_cost_items(cost_items_rolled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also create a revenue flow; for the purposes of LCOE, that is simply energy generation per year treated as a flow (i.e. it can be discounted)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Revenue in form of MWh per year\n",
    "revenue_data = evl.create_cash_flow((operation_start, operation_end, mwh_per_year))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LCOE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, the LCOE can be calculated!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LCOE with a discount rate of 7.00%: 133.89 $/MWh\n"
     ]
    }
   ],
   "source": [
    "lcoe = evl.calculate_lcoe(cost_items, revenue_data, discount_rate, present_year)\n",
    "print(f\"LCOE with a discount rate of {discount_rate*100:.2f}%: {lcoe:.2f} $/MWh\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Effect of Discounting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you might have noticed, the discount rate only entered the calculation at the very end. Even though the code in `evaluate.py` allows for assigning discount rates to the individual cash flows, it is also possible _not_ to do that and apply a default discount rate at the very end, when net present value is calculated. Therefore, we can for example calculate the LCOE if 0% discounting is assumed (both for costs and for revenue/energy production)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LCOE with a discount rate of 0.00%: 42.21 $/MWh\n"
     ]
    }
   ],
   "source": [
    "zero_discount_rate = 0\n",
    "zero_lcoe = evl.calculate_lcoe(\n",
    "    cost_items, revenue_data, zero_discount_rate, present_year\n",
    ")\n",
    "print(\n",
    "    f\"LCOE with a discount rate of {zero_discount_rate*100:.2f}%: {zero_lcoe:.2f} $/MWh\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That is a significant difference! That is due to the fact that the operational life of a nuclear power plant is very long and 7% discounting effectively negates any revenue (and cost) after 20-30 years. This is shown below, where the discount factor is plotted. It is an interactive plot so you can try different discount rates as well. _(don't worry if you don't understand the code, it is basically just plotting)_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
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             {
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             {
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            "label": "8.0%",
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             {
              "title": "Discounting Function Over Time (Discount Rate: 9.0%)"
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            "label": "9.0%",
            "method": "update"
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    "# Define the function to calculate the discount factor\n",
    "def discounting_function(time, discount_rate):\n",
    "    return 1 / (1 + discount_rate) ** time\n",
    "\n",
    "\n",
    "# Create time range\n",
    "time = np.linspace(0, 80, 500)  # Time from 0 to 80 years\n",
    "\n",
    "# Create the figure\n",
    "fig = go.Figure()\n",
    "\n",
    "# Add the initial discounting function\n",
    "fig.add_trace(\n",
    "    go.Scatter(\n",
    "        x=time,\n",
    "        y=discounting_function(time, discount_rate),\n",
    "        mode=\"lines\",\n",
    "        name=f\"Discount Rate: {discount_rate*100:.1f}%\",\n",
    "        line=dict(width=2),\n",
    "    )\n",
    ")\n",
    "\n",
    "# Define the slider steps\n",
    "steps = []\n",
    "for rate in np.linspace(0.01, 0.1, 10):  # Rates from 1% to 10%\n",
    "    step = dict(\n",
    "        method=\"update\",\n",
    "        label=f\"{rate*100:.1f}%\",\n",
    "        args=[\n",
    "            {\"y\": [discounting_function(time, rate)]},  # Update y values\n",
    "            {\n",
    "                \"title\": f\"Discounting Function Over Time (Discount Rate: {rate*100:.1f}%)\"\n",
    "            },  # Update title\n",
    "        ],\n",
    "    )\n",
    "    steps.append(step)\n",
    "\n",
    "# Add the slider\n",
    "fig.update_layout(\n",
    "    title=f\"Discounting Function Over Time (Discount Rate: {discount_rate*100:.1f}%)\",\n",
    "    xaxis_title=\"Time (Years)\",\n",
    "    yaxis_title=\"Discount Factor\",\n",
    "    template=\"plotly_white\",\n",
    "    font=dict(size=14),\n",
    "    sliders=[\n",
    "        {\n",
    "            \"active\": 6,  # Index of 7% in the slider steps (6th step if range is 1%-10% in 20 increments)\n",
    "            \"currentvalue\": {\"prefix\": \"Discount Rate: \", \"font\": {\"size\": 14}},\n",
    "            \"steps\": steps,\n",
    "        }\n",
    "    ],\n",
    ")\n",
    "\n",
    "# Show the plot\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seeing the effect of discounting, an interesting question arises. Typically O&M, fuel and decomissioning costs are taken to be negligible for nuclear power plants. Of course, we saw that the LCOE drops rapidly by eliminating the discount rate -- but what does that do to the cost distribution? For that, we use another function from the `evaluate.py`, which uses the cost item dictionary to calculate a dictionary of discounted cash flows -- and this can be done with whichever discount rate we choose!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "discounted_costs_default = evl.discount_cash_flows(\n",
    "    cost_items, discount_rate, present_year\n",
    ")\n",
    "discounted_costs_zero = evl.discount_cash_flows(\n",
    "    cost_items, zero_discount_rate, present_year\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And this we can now plot side by side!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
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   "source": [
    "# Normalize values to 100% for each dictionary\n",
    "total1 = sum(discounted_costs_default.values())\n",
    "total2 = sum(discounted_costs_zero.values())\n",
    "\n",
    "labels = list(discounted_costs_default.keys())  # Extract keys for labels\n",
    "values1 = [\n",
    "    v / total1 * 100 for v in discounted_costs_default.values()\n",
    "]  # Normalize to percentages for dict1\n",
    "values2 = [\n",
    "    v / total2 * 100 for v in discounted_costs_zero.values()\n",
    "]  # Normalize to percentages for dict2\n",
    "\n",
    "# Create subplots for side-by-side pie charts\n",
    "subplot_titles = [\n",
    "    f\"Discount rate {discount_rate*100:.0f}%, LCOE {lcoe:.0f} $/MWh\",\n",
    "    f\"Discount rate {zero_discount_rate*100:.0f}%, LCOE {zero_lcoe:.0f} $/MWh\",\n",
    "]\n",
    "\n",
    "fig = sp.make_subplots(\n",
    "    rows=1,\n",
    "    cols=2,\n",
    "    specs=[[{\"type\": \"domain\"}, {\"type\": \"domain\"}]],\n",
    "    subplot_titles=subplot_titles,\n",
    ")\n",
    "\n",
    "# Add pie chart for dictionary 1\n",
    "fig.add_trace(\n",
    "    go.Pie(labels=labels, values=values1, name=subplot_titles[0]), row=1, col=1\n",
    ")\n",
    "\n",
    "# Add pie chart for dictionary 2\n",
    "fig.add_trace(\n",
    "    go.Pie(labels=labels, values=values2, name=subplot_titles[1]), row=1, col=2\n",
    ")\n",
    "\n",
    "# Update layout for better appearance\n",
    "fig.update_layout(\n",
    "    title_text=\"Cost Breakdown for Different Discount Rates\",\n",
    "    title_x=0.5,  # Center the title\n",
    "    title_y=0.95,\n",
    "    showlegend=True,\n",
    "    annotations=[\n",
    "        dict(\n",
    "            x=0.20,  # x-position of the title for the first chart\n",
    "            y=1.05,  # Adjust space above the chart title\n",
    "            text=subplot_titles[0],\n",
    "            showarrow=False,\n",
    "            font=dict(size=14),\n",
    "        ),\n",
    "        dict(\n",
    "            x=0.80,  # x-position of the title for the second chart\n",
    "            y=1.05,  # Adjust space above the chart title\n",
    "            text=subplot_titles[1],\n",
    "            showarrow=False,\n",
    "            font=dict(size=14),\n",
    "        ),\n",
    "    ],\n",
    ")\n",
    "\n",
    "# Show the plot\n",
    "fig.show()"
   ]
  }
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